My LCGA analysis suggests a two class model with different intercepts. Both these classes show no significant linear or quadratic growth. One of the advantages of GMM is that it estimates within class variance. Given my LCGA results, would it be worthwhile to run GMM models?

In GMM, the intercept growth factor would have a variance unlike in LCGA. Allowing within-class variability may also change the significance of the other growth factors because the class formation will be different.

I'd like to estimate a GMM for a binary outcome where the data set has a large number of time points (48) and approximately 2000 cases. Is this going to be too computationally demanding? Is there any way to speed things up? So far, relatively simple 2-class models are taking multiple days to fit... if they fit at all.

Thanks, Linda. The user's guide says not to use the STARTS setting when the number of integration points is > 500. When I run the model, it says that the number of integration points is 3375 with 3 dimensions. Is it still okay to use the STARTS setting?